OVERVIEW OF THE PROJECT
Cloud computing is
a model for enabling ubiquitous, convenient, on-demand net-
to a shared pool of configurable computing resources that can be rapidly
provisioned and released with service provider interaction .It is a new
delivering on-demand resources for customers through internet.
A service is a mech- anism
is capable of providing one or more
which it is possible
to use in compliance with
restrictions and rules and through an in- terface
.There are three services models in cloud.
are Software as a service:
A software or application that is executing on a vendors infrastructure is
recognized as a service provided
that the consumer has limited permission to access
and the provision
is through a thin client
or a program interface for sending data and
The consumer is
the application providers
has lim- ited authority to configure some settings. Platform
as a service: In this services
model, the service
vendor provides moderate
basic requisites, including
the operating system, network and
to allow the
consumer to develop ac-
quired applications or software and manage their configurable settings. Infrastructure
as a service: The cloud service consumer has developed the required applications and needs only a basic infrastructure. In
such cases, processors,
networks, and storage can
be provided by
vendors as services with consumer provisions.
Cloud service Ranking is needed to
cloud service consumers to choose appropriate cloud service from a pool of available cloud services. The Qos parameters such
as response time, availability, throughput,
etc. are used to rank
the cloud services based upon consumers require-
ments. Particle swarm optimization (PSO) is a computational method that
problem by iteratively trying to improve a candidate solution
with regard to a given measure of quality. PSO optimizes a
problem by having a population of candidate so- lutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae.
1.2 PROBLEM STATEMENT
service ranking approach only few quantifiable parameters QoS attributes were
used for ranking. Several non-quantifiable QoS attributes have major impact in
the ranking and selection process. Also, static ranking of cloud services may
provide inappropriate cloud service to cloud service consumers as the
requirements of one consumer vary with another. The dynamic ranking and
selection of cloud services is solved by designing a cloud broker model with
several components work together to perform Cloud Service Ranking and Selection using Particle Swarm Optimization.
1.3 CHALLENGES AND SCOPE
The accuracy achieved through this
project is 94% which can be increased further.
The classifiers considered can be
changed further to improve efficiency.
The proposed project is subject to text
mining and so still other mining techniques like spatial and correlation
techniques can be used.
In this paper, they survey state-of-the-art Cloud services selection approaches, which are analyzed from the following five perspectives: decision-making techniques; data representation models; parameters and
characteristics of Cloud services; contexts, purposes. After
summarizing the approaches they
identify the pri- mary
research issues in Cloud service
approaches for Cloud service selection: Chang etal.(2012) developed a
algorithm to select Cloud storage providers that can maximize the data survival prob- ability
or the amount of surviving data, subject
to a fixed budget. They formulated the problem of multiple storage
service provider selection
into a probability model with clearly defined object functions and cost measurements. The availability of the storage service is quantitatively analyzed by
two methods minimum failure probability with a given budget, and maximum validity
with a given budget. Sundareswaran
etal.(2012) employed a greedy algorithm- based method for Cloud service
selection. They pro- posed the use of the B+ tree to index
providers (i.e.CSPindexing) and encode services
The indexing structure supports the indexing of service
the modeling of their relative importance, as ordered by users. It enables fast
information retrieval for decision makers. Martens
(2012) developed a scalable
mathematical decision model
for discrete dynamic opti- mization problems in Cloud service selection. The model
helps organizations to iden- tify
suitable Cloud services by minimizing costs and risks. An AHP-based approach is
measure the relative importance of the services
in a business process and the relative
importance of security parameters in a risk evaluation process.
Finally, decisions can be made by solving the formulated mathematical models. Optimiza- tion techniques, such as linear, non-
linear, and genetic algorithms, are recommended as the tools for solving
models, depending on
the specific service outsourcing
narios. Identified issues: The open issues on contemporary Cloud service
selection approaches are
1. Lack of a marketplace
for Cloud service
publication and transac- tion: Cloud services do not have a standard for service publication
and registry. The
lack of detailed service QoS information makes it difficult for service users to make educated purchasing
decisions. Cloud service
and make comments on services, but there is no feedback from users.
2. Lack of normalization
for Cloud service
description serving different kinds of users: The flourishing of Cloud services highlights the need for a unified specification for Cloud services. A high level of abstraction and support for
the simple publication, discovery, selection,
and use of resources for both
service providers and
3. Lack of a search
engine system for the
automatic identification and
updating of Cloud service information: Cloud service specification lacks a standard form, especially for IaaS and PaaS. The service information is typically published as plain text on a Web page, which usually
narrows to a functional description rather than being complete enough to include tech- nical details.
Such incompleteness prevents keyword-based search engines returning accurate services.
4. Lack of an efficient
means to deal with qualitative parameters and fuzzy expression:
Qualitative non-functional properties such as security and availabil-
ity increase the fuzziness of service evaluation. Current techniques are more
quantitative criteria that can be measured via
precise numerical values
as response time, storage space and network latency. Hence an efficient
method of han- dling uncertainty and fuzziness in service specification and user requirements needs to be taken into account for
the chosen services.
concern on multi-tenancy service selection.
of an advanced multi-criteria-based measurement of
7. Lack of consideration of the interdependency of
8.Lack of long term performance predication and dynamic application strategy.
DESIGN AND IMPLEMENTATION
3.1 EXISTING SYSTEM
The data mining technique that is being
used comprise of a model that helps in training the train data set. The model
is made up of techniques without any Cross Validations and repeats. Hence the
obtained accuracy is around 92%. The false positive rate is also high. Though
all kind of vulnerabilities are considered, the results of all vulnerabilities
are of the same accuracy. The vulnerabilities include XSS, SQL Injection.
3.2 PROPOSED SYSTEM
all web applications is moving from a traditional deployment strategy to an
on-demand cloud environment. It is highly difficult for the cloud service
consumers to choose wisely between the
available cloud providers. On the other hand, each and every cloud provider may
have interest on different parameters to be set for their infrastructures.
Also, there is no common registry to register the service level agreement of
cloud service as that of the web services. Hence, it becomes difficult for the
consumers to choose appropriately the required services and thereby cloud
DESIGN AND IMPLEMENTATION
4.1 OVERALL DESCRIPTION
The proposed cloud broker
architecture has three components. They
are Cloud Service Consumer; the
individual or an organization that requires a cloud service either to deploy an
application or for application development, Cloud Broker; is the middleware that receives input
from the cloud consumers as well as the cloud service providers. It checks the
service level objectives with that of the service level agreement and makes the
decision processing to rank and thereby select the cloud service. Cloud Service Provider; is an entity that provides
cloud services to the end users or cloud service consumers.
4.2 ARCHITECTURE DIAGRAM
Broker has two databases SLA repository and
Qos information reposi- tory and has probation manager, rank manager, co-ordination
Agent and search agent. The SLAs of cloud service providers
are stored in the SLA Repository of cloud broker. The SLA document consists of the quantifiable and non- quantifiable Qos parameters
which include service name, cloud
provider, security, availability, processor speed,
cost per hour,
storage, bandwidth, performance, etc.,
The Probation Manager : takes SLA from SLA repository and checks the parameters of SLA during the probation period. After the validation it informs the rank manager
with updated parameters.
The Rank Manager: has
and updates the
with SLA parameter given by probation manager. Rank table contains ranking of
cloud services according to the SLA parameters. If a service is longer
by a consumer, then rank manager gives the service to probation
manager for validation.
information repository: feedback of the past customer experience
in Qos Information Repository.
4.3 LIST OF MODULES
1. Build SLA
and Design Cloud Broker
– Probation Manager
– Rank Manager
2. Build Qos Information
Repository & add into Cloud Broker
– Co-ordination Agent
– Search Agent
Integration of cloud service consumers requirements with broker
Cloud Service Ranking and Selection using PSO
The SLA from cloud service providers for
The SLA document consists
of the quantifiable Qos parametersuca as service
Figure 2: Input SLA
cloud provider, security, availability, processor
speed, cost per hour, storage and non Qos
bandwidth etc. The SLA parameters are collected and stored in Mysql server.
Design of Cloud Broker
The cloud broker
has four entities Probation Manager,
Rank Manager ,Co-ordination Agent,
Search Agent. Using cloudsim, the broker is created with entities along
Step1:Simulation of Probation Manager
Figure 5: Probation
gets the SLA
parameters from the database and
populate the table with SLA.
Figure 6: Probation
Manager gets SLA’s
5.1 HARDWARE REQUIREMENTS
1 GB and above
Dual core and above
80 GB and above
5.2 SOFTWARE REQUIREMENTS
CONCLUSION AND FUTURE WORK
data thus has been filtered to figure out what are the data that are vulnerable
and non-vulnerable data. The improved accuracy helps in better filtering of
data. The future work is to implement Ensembling models in order to achieve
still better accuracy results. Also the method of preventing the vulnerable
data can also be proposed thereby preventing the impact of vulnerable data
during the transmission of it and safeguarding the entire system.
is a general term for combining many classifiers by averaging or voting. It is
a form of meta learning in that it focuses on how to merge results of arbitrary
underlying classifiers. Generally, ensembles of classifiers perform better than
single classifiers, and the averaging process allows for more granularity of
choice in the bias-variance tradeoff.
of ensemble techniques include bagging, boosting, model
averaging, and weak learner theory.
An obvious strategy is
thus to implement as many different solvers as possible and ensemble them all
together, a sort of “More Models are Better” approach.
Text Mining is the key
to determine the vulnerable data at the source and efficient methods in
adopting text mining will improve the mining results.
OUTPUT OF MODULES
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